
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
plt.rcParams['figure.figsize'] = [12, 6]
plt.rcParams.update({'font.size': 12})


df = pd.read_excel('columns.xlsx')
col = df['Symbol'].values
col = col.tolist()


df_stableFeedFlow = pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/NOC_stableFeedFlow_outputs.csv',header=None)
df_stableFeedFlow.set_index = df_stableFeedFlow.iloc[:,0]
df_stableFeedFlow = df_stableFeedFlow.drop(columns=0)
df_stableFeedFlow.columns= col
df_stableFeedFlow.sample(5)


df_varyingFeedFlow=pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/NOC_varyingFeedFlow_outputs.csv',header=None)
df_varyingFeedFlow.set_index = df_varyingFeedFlow.iloc[:,0]
df_varyingFeedFlow = df_varyingFeedFlow.drop(columns=0)
df_varyingFeedFlow.columns= col
df_varyingFeedFlow.sample(5)


df_CAB_valveLeak = pd.read_csv(r'https://raw.githubusercontent.com/AshuPraja13/FCC-abnormality-detection/main/CAB_valveLeak_outputs.csv',header=None)
df_CAB_valveLeak.set_index = df_CAB_valveLeak.iloc[:,0]
df_CAB_valveLeak = df_CAB_valveLeak.drop(columns=0)
df_CAB_valveLeak.columns= col
df_CAB_valveLeak.sample(5)


df_CAB_valveLeak.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1440 entries, 0 to 1439
Data columns (total 46 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   F3           1440 non-null   float64
 1   Tatm         1440 non-null   float64
 2   T1           1440 non-null   float64
 3   P4           1440 non-null   float64
 4   deltaP       1440 non-null   float64
 5   P6           1440 non-null   float64
 6   Fair         1440 non-null   float64
 7   T3           1440 non-null   float64
 8   T2           1440 non-null   float64
 9   Tr           1440 non-null   float64
 10  Treg         1440 non-null   float64
 11  Lsp          1440 non-null   float64
 12  Tcyc         1440 non-null   float64
 13  Tcyc - Treg  1440 non-null   float64
 14  Cco,sg       1440 non-null   int64  
 15  Co2,sg       1440 non-null   float64
 16  P5           1440 non-null   float64
 17  V4           1440 non-null   float64
 18  V6           1440 non-null   float64
 19  V7           1440 non-null   float64
 20  V3           1440 non-null   float64
 21  V1           1440 non-null   float64
 22  V2           1440 non-null   float64
 23  Frgc         1440 non-null   int64  
 24  Fsc          1440 non-null   int64  
 25  ACAB         1440 non-null   float64
 26  AWGC         1440 non-null   float64
 27  F5           1440 non-null   float64
 28  F7           1440 non-null   float64
 29  Fsg          1440 non-null   float64
 30  FV11         1440 non-null   int64  
 31  P1           1440 non-null   float64
 32  P2           1440 non-null   float64
 33  FLPG         1440 non-null   float64
 34  FLN          1440 non-null   float64
 35  FHN          1440 non-null   float64
 36  FLCO         1440 non-null   float64
 37  FSlurry      1440 non-null   float64
 38  FReflux      1440 non-null   float64
 39  Tfra         1440 non-null   float64
 40  T10          1440 non-null   float64
 41  T20          1440 non-null   float64
 42  V9           1440 non-null   float64
 43  V8           1440 non-null   float64
 44  V10          1440 non-null   float64
 45  V11          1440 non-null   float64
dtypes: float64(42), int64(4)
memory usage: 517.6 KB


df_CAB_valveLeak.describe().T


sns.heatmap(df_CAB_valveLeak.corr(),cmap='coolwarm')

<AxesSubplot:>


for n,i in enumerate(df_stableFeedFlow.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_stableFeedFlow[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


for n,i in enumerate(df_varyingFeedFlow.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_varyingFeedFlow[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


for n,i in enumerate(df_CAB_valveLeak.columns):
    plt.figure(figsize=(12,2))
    plt.plot(df_CAB_valveLeak[i])
    plt.xlabel('time (mins)')
    plt.ylabel(i)
    plt.title(df[df['Symbol']==i]['Description'].values)
    plt.show()


from sklearn.preprocessing import StandardScaler
ss = StandardScaler()


X = ss.fit_transform(df_stableFeedFlow)


from sklearn.decomposition import PCA
pca = PCA()


X_pca = pca.fit_transform(X)


plt.figure(figsize=(15,6))
sns.set_style('whitegrid')
sns.lineplot(x=list(range(1,47)), y=np.cumsum(pca.explained_variance_ratio_), drawstyle='steps-pre')
sns.lineplot(x=list(range(0,46)),y=np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Eigen Values')
plt.ylabel('Ratio of Variance')
plt.title('Variance by each Eigen Value')
plt.show()


pca = PCA(n_components=10)
X_pca = pca.fit_transform(X)


plt.figure(figsize=(15,6))
sns.set_style('whitegrid')
sns.lineplot(x=list(range(1,11)), y=np.cumsum(pca.explained_variance_ratio_), drawstyle='steps-pre')
sns.lineplot(x=list(range(0,10)),y=np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('Eigen Values')
plt.ylabel('Ratio of Variance')
plt.title('Variance by each Eigen Value')
plt.show()


X_train = X.reshape(2880,46,1)


# del model


model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(256,return_sequences=True),input_shape=(46,1)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128,return_sequences=True)))
model.add(tf.keras.layers.Dropout(0.2))
model.add(tf.keras.layers.Dense(1))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.summary()

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 bidirectional (Bidirection  (None, 46, 512)           528384    
 al)                                                             
                                                                 
 dropout (Dropout)           (None, 46, 512)           0         
                                                                 
 bidirectional_1 (Bidirecti  (None, 46, 256)           656384    
 onal)                                                           
                                                                 
 dropout_1 (Dropout)         (None, 46, 256)           0         
                                                                 
 dense (Dense)               (None, 46, 1)             257       
                                                                 
=================================================================
Total params: 1185025 (4.52 MB)
Trainable params: 1185025 (4.52 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________


model.fit(X_train,X_train,epochs=30)

Epoch 1/30
90/90 [==============================] - 56s 493ms/step - loss: 0.1484 - mae: 0.2095
Epoch 2/30
90/90 [==============================] - 48s 538ms/step - loss: 0.0029 - mae: 0.0376
Epoch 3/30
90/90 [==============================] - 30s 333ms/step - loss: 0.0025 - mae: 0.0338
Epoch 4/30
90/90 [==============================] - 22s 245ms/step - loss: 0.0022 - mae: 0.0316
Epoch 5/30
90/90 [==============================] - 23s 252ms/step - loss: 0.0020 - mae: 0.0301
Epoch 6/30
90/90 [==============================] - 27s 305ms/step - loss: 0.0019 - mae: 0.0291
Epoch 7/30
90/90 [==============================] - 40s 441ms/step - loss: 0.0018 - mae: 0.0281
Epoch 8/30
90/90 [==============================] - 40s 440ms/step - loss: 0.0018 - mae: 0.0276
Epoch 9/30
90/90 [==============================] - 40s 444ms/step - loss: 0.0017 - mae: 0.0270
Epoch 10/30
90/90 [==============================] - 39s 438ms/step - loss: 0.0017 - mae: 0.0267
Epoch 11/30
90/90 [==============================] - 40s 444ms/step - loss: 0.0016 - mae: 0.0263
Epoch 12/30
90/90 [==============================] - 40s 445ms/step - loss: 0.0016 - mae: 0.0262
Epoch 13/30
90/90 [==============================] - 40s 443ms/step - loss: 0.0016 - mae: 0.0259
Epoch 14/30
90/90 [==============================] - 40s 441ms/step - loss: 0.0016 - mae: 0.0256
Epoch 15/30
90/90 [==============================] - 40s 441ms/step - loss: 0.0015 - mae: 0.0253
Epoch 16/30
90/90 [==============================] - 40s 442ms/step - loss: 0.0015 - mae: 0.0252
Epoch 17/30
90/90 [==============================] - 39s 439ms/step - loss: 0.0015 - mae: 0.0250
Epoch 18/30
90/90 [==============================] - 40s 440ms/step - loss: 0.0015 - mae: 0.0251
Epoch 19/30
90/90 [==============================] - 40s 439ms/step - loss: 0.0015 - mae: 0.0250
Epoch 20/30
90/90 [==============================] - 40s 440ms/step - loss: 0.0015 - mae: 0.0249
Epoch 21/30
90/90 [==============================] - 40s 445ms/step - loss: 0.0015 - mae: 0.0248
Epoch 22/30
90/90 [==============================] - 39s 436ms/step - loss: 0.0014 - mae: 0.0245
Epoch 23/30
90/90 [==============================] - 39s 439ms/step - loss: 0.0014 - mae: 0.0246
Epoch 24/30
90/90 [==============================] - 39s 437ms/step - loss: 0.0014 - mae: 0.0245
Epoch 25/30
90/90 [==============================] - 39s 436ms/step - loss: 0.0014 - mae: 0.0244
Epoch 26/30
90/90 [==============================] - 41s 456ms/step - loss: 0.0014 - mae: 0.0244
Epoch 27/30
90/90 [==============================] - 40s 443ms/step - loss: 0.0014 - mae: 0.0243
Epoch 28/30
90/90 [==============================] - 40s 445ms/step - loss: 0.0014 - mae: 0.0243
Epoch 29/30
90/90 [==============================] - 41s 457ms/step - loss: 0.0014 - mae: 0.0241
Epoch 30/30
90/90 [==============================] - 44s 487ms/step - loss: 0.0014 - mae: 0.0243

<keras.src.callbacks.History at 0x2b58c3fd790>


error_ae = []
for i in range(X.shape[0]):
    y_pred = model.predict(X[i].reshape(1,46,1),verbose=None)[0,:,0]
    error_ae.append(np.abs(X[i]-y_pred).sum())
AE_CL = np.percentile(error_ae,99)


pd.Series(error_ae).plot()
plt.hlines(AE_CL,0,len(error_ae),colors='red',linestyles='--')

<matplotlib.collections.LineCollection at 0x2b58ac00430>


X_reconstructed = np.dot(X_pca,pca.components_)
error_pca = X-X_reconstructed
Q_train = np.sum(np.abs(error_pca),axis=1)
Q_CL = np.percentile(Q_train,99)
# Q_train plot with CL
plt.figure()
plt.plot(Q_train, color='black')
plt.plot([1,len(Q_train)],[Q_CL,Q_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('Q metric: training data')
plt.title(f'Q metrix is max: {Q_train.max()} at:{Q_train.argmax()}mins')
plt.show()


lambda_ = np.diag(pca.explained_variance_)
lambda_inv = np.linalg.inv(lambda_)
T_train = np.zeros(X_pca.shape[0])
for i in range(X_pca.shape[0]):
    T_train[i] = np.dot(np.dot(X_pca[i],lambda_inv),X_pca[i].T)
T_CL = np.percentile(T_train,99)
# T2_train plot with CL
plt.figure()
plt.plot(T_train, color='black')
plt.plot([1,len(T_train)],[T_CL,T_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('T$^2$ metric: training data')
plt.title(f'T$^2$ metrix is max: {np.array(T_train).max()} at:{np.array(T_train).argmax()}mins')
plt.show()


cosine = []
ed = []
for i in range(X.shape[0]):
    v1 = X[i]
    v2 = np.dot(X_pca,pca.components_)[i]
    cosine.append(np.dot(v1,v2)/(np.linalg.norm(v1)*np.linalg.norm(v2)))
    ed.append(np.linalg.norm(v1 - v2))
C_CL = np.min(cosine)
E_CL = np.percentile(ed,99)
# pd.Series(ed).plot(color='black')
# plt.plot([1,len(ed)],[E_CL,E_CL], linestyle='--',color='red', linewidth=2)
# plt.show()
pd.Series(cosine).plot(color='black')
plt.plot([1,len(cosine)],[C_CL,C_CL], linestyle='--',color='red', linewidth=2)
plt.xlabel('Sample #')
plt.ylabel('Cosine similarity metric: training data')
plt.title(f'Cosine Similarity')
plt.show()


Q_CL,T_CL,C_CL,E_CL,AE_CL

(4.123113215519088,
 20.424350352568787,
 0.9427550112367161,
 0.9281694746755792,
 0.4572564523844304)


def Q_test(X,X_pca,pca_components_,Q_CL):
    X_reconstructed = np.dot(X_pca,pca_components_)
    error_pca = X-X_reconstructed
    Q_train = np.sum(np.abs(error_pca),axis=1)
    # Q_train plot with CL
    plt.figure()
    plt.plot(Q_train, color='black')
    plt.plot([1,len(Q_train)],[Q_CL,Q_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('Q metric: training data')
    plt.title(f'Q metrix is max: {Q_train.max()} at:{Q_train.argmax()}mins')
    plt.show()
    return error_pca


def T_test(X_pca,explained_variance_,TCL):
    lambda_ = np.diag(pca.explained_variance_)
    lambda_inv = np.linalg.inv(lambda_)
    T_train = np.zeros(X_pca.shape[0])
    for i in range(X_pca.shape[0]):
        T_train[i] = np.dot(np.dot(X_pca[i],lambda_inv),X_pca[i].T)
    # T2_train plot with CL
    plt.figure()
    plt.plot(T_train, color='black')
    plt.plot([1,len(T_train)],[T_CL,T_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('T$^2$ metric: training data')
    plt.title(f'T$^2$ metrix is max: {np.array(T_train).max()} at:{np.array(T_train).argmax()}mins')
    plt.show()


def cosine(X,X_transformed,pca_components_,C_CL,E_CL):
    cosine = []
    ed = []
    for i in range(X.shape[0]):
        v1 = X[i]
        v2 = np.dot(X_transformed,pca_components_)[i]
        cosine.append(np.dot(v1,v2)/(np.linalg.norm(v1)*np.linalg.norm(v2)))
        ed.append(np.linalg.norm(v1 - v2))
#     pd.Series(ed).plot(color='black')
#     plt.plot([1,len(ed)],[E_CL,E_CL], linestyle='--',color='red', linewidth=2)
#     plt.xlabel('Sample #')
#     plt.ylabel('Eucledian Distance metric: training data')
#     plt.show()
    pd.Series(cosine).plot(color='black')
    plt.plot([1,len(cosine)],[C_CL,C_CL], linestyle='--',color='red', linewidth=2)
    plt.xlabel('Sample #')
    plt.ylabel('Cosine similarity metric: training data')
    plt.title(f'Cosine Similarity')
    plt.show()


def autoencoder(df_test,CL):
    X_test = ss.transform(df_test)
    error_ae = []
    error_sum = []
    for i in range(X_test.shape[0]):
        y_pred = model.predict(X_test[i].reshape(1,46,1),verbose=None)[0,:,0]
        error_ae.append(np.abs(X_test[i]-y_pred))
        error_sum.append(np.abs(X_test[i]-y_pred).sum())
    error_ae=np.array(error_ae)
    pd.Series(error_sum).plot(color = 'black')
    plt.hlines(CL,0,len(error_ae),colors='red',linestyles='--')
    plt.xlabel('Sample #')
    plt.ylabel('Reconstruction error by Autoencoder')
    return error_ae


X = ss.transform(df_varyingFeedFlow)
X_test = pca.transform(X)


error_pca = Q_test(X,X_test,pca.components_,Q_CL)


T_test(X_test,pca.explained_variance_,T_CL)


cosine(X,X_test,pca.components_,C_CL,E_CL)


error_ae = autoencoder(df_varyingFeedFlow,AE_CL)


X = ss.transform(df_CAB_valveLeak)
X_test = pca.transform(X)


error_pca = Q_test(X,X_test,pca.components_,Q_CL)


T_test(X_test,pca.explained_variance_,T_CL)


cosine(X,X_test,pca.components_,C_CL,E_CL)


error_ae = autoencoder(df_CAB_valveLeak,AE_CL)


#%% Q contribution
error = np.abs(error_pca).sum(axis=1)
cum = []
for index,value in enumerate(error):
    if (value>Q_CL) and (len(cum)<15):
        previous_val = value
        cum.append(value)
        if len(cum) == 15:
            sample = index
            break
    else:
        cum=[]
# sample = ((pd.Series(error_pca.sum(axis=1))-pd.Series(error_pca.sum(axis=1)).shift()).abs()).argmax()
print('Time-',sample,'mins')
error_test_sample = error_pca[sample]
Q_contri = np.abs(error_test_sample) # *error_test_sample # vector of contributions

Time- 141 mins


plt.figure(figsize=[15,4])
plt.bar(['variable ' + str((i+1)) for i in range(len(Q_contri))], Q_contri)
plt.xticks(rotation = 80)
plt.ylabel('Q contributions')
plt.show()


plt.figure(figsize=(15,40))
print('Time-',sample,'mins')
for i,n in enumerate(np.argsort(Q_contri)[:-11:-1]):
    plt.subplot(5,2,i+1)
    plt.plot(df_CAB_valveLeak.iloc[:,n],'blue', linewidth=1)
    plt.xlabel('time (mins)')
    plt.ylabel(df['Symbol'][n])
    plt.title(df['Description'][n])
    plt.show

Time- 141 mins


#%% Autoencoder Error
error = np.abs(error_ae).sum(axis=1)
cum = []
for index,value in enumerate(error):
    if (value>AE_CL) and (len(cum)<15):
        previous_val = value
        cum.append(value)
        if len(cum) == 15:
            sample = index
            break
    else:
        cum=[]
# sample = ((pd.Series(error_ae.sum(axis=1))-pd.Series(error_ae.sum(axis=1)).shift()).abs()).argmax()
print('Time-',sample,'mins')
error_test_sample = error_pca[sample]
Q_contri = np.abs(error_test_sample) # *error_test_sample # vector of contributions

Time- 186 mins


plt.figure(figsize=(15,45))
print('Time-',sample,'mins')
for i,n in enumerate(np.argsort(error_ae[sample])[:-11:-1]):
    plt.subplot(5,2,i+1)
    plt.plot(df_CAB_valveLeak.iloc[:,n],'blue', linewidth=1)
    plt.xlabel('time (mins)')
    plt.ylabel(df['Symbol'][n])
    plt.title(df['Description'][n])
    plt.show

Time- 186 mins

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
2510	164.81	78.793	460.44	34.4	-6.4	28	2.6791	1565.6	615.99	969.03	...	1641.5	214.26	2972.2	313.64	509.70	627.94	47.332	49.465	49.062	47.113
2250	165.12	79.937	461.05	34.4	-6.4	28	2.6831	1563.9	616.00	969.01	...	1648.5	210.68	3010.2	314.99	509.90	628.21	45.753	49.871	49.654	47.494
1550	165.11	76.402	461.20	34.4	-6.4	28	2.6796	1562.9	616.00	968.95	...	1644.8	212.73	2952.6	311.70	509.74	628.01	49.726	49.476	49.609	47.294
1035	165.12	79.052	460.65	34.4	-6.4	28	2.6847	1566.3	615.99	969.04	...	1647.3	207.21	2989.2	314.01	509.85	628.00	46.855	49.687	48.861	47.431
2047	164.86	79.886	461.45	34.4	-6.4	28	2.6766	1559.8	616.00	968.97	...	1633.3	220.75	2972.4	314.41	509.57	627.92	46.378	49.340	49.324	46.653

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
5059	162.09	80.015	460.63	34.4	-6.4	28	2.6343	1547.7	616.00	969.02	...	1592.0	231.02	2575.4	307.04	505.86	622.37	53.798	43.872	47.175	44.676
2478	164.47	79.050	461.41	34.4	-6.4	28	2.6695	1557.7	616.00	968.95	...	1630.7	222.70	2904.2	312.66	509.04	627.10	48.261	48.494	49.154	46.557
9389	165.23	80.033	460.58	34.4	-6.4	28	2.6840	1567.5	616.01	969.00	...	1643.5	211.99	3015.5	315.17	509.97	628.21	45.564	49.931	48.979	47.204
1497	166.99	75.731	461.42	34.4	-6.4	28	2.7136	1573.2	616.00	969.01	...	1675.3	211.15	3280.2	317.02	512.42	631.67	44.666	53.997	51.098	48.790
82	165.05	76.114	460.42	34.4	-6.4	28	2.6792	1567.3	615.99	968.97	...	1638.2	215.91	2943.7	311.38	509.69	628.02	50.118	49.383	49.131	46.919

	F3	Tatm	T1	P4	deltaP	P6	Fair	T3	T2	Tr	...	FLCO	FSlurry	FReflux	Tfra	T10	T20	V9	V8	V10	V11
197	165.18	77.406	461.11	34.4	-6.4	28.0	2.6816	1563.8	615.99	968.98	...	1639.0	211.98	2960.0	312.47	509.75	627.97	48.717	49.468	48.745	46.961
806	164.92	79.932	460.89	34.4	-6.4	28.0	2.6809	1563.7	616.00	969.02	...	1645.1	215.73	3011.8	315.08	509.89	628.31	45.701	49.881	49.905	47.298
902	164.92	79.636	461.34	34.4	-6.4	28.0	2.6813	1560.9	616.00	969.04	...	1644.2	213.84	2999.0	314.65	509.84	628.15	46.168	49.741	49.456	47.251
1215	165.20	77.640	460.40	34.4	-6.4	28.0	2.6815	1568.3	615.99	968.96	...	1648.9	212.58	2992.6	313.26	509.96	628.34	47.964	49.871	49.755	47.506
31	165.02	75.406	461.02	34.4	-6.4	28.0	2.6804	1563.5	615.99	968.99	...	1640.3	214.24	2945.1	310.94	509.80	628.09	50.727	49.484	48.764	47.031

	count	mean	std	min	25%	50%	75%	max
F3	1440.0	165.012694	1.582447e-01	164.530000	164.890000	165.020000	165.140000	165.480000
Tatm	1440.0	78.342193	1.491834e+00	74.981000	77.191500	78.746000	79.687250	80.070000
T1	1440.0	460.899750	3.939347e-01	459.580000	460.650000	460.910000	461.140000	462.050000
P4	1440.0	34.400000	8.884870e-13	34.400000	34.400000	34.400000	34.400000	34.400000
deltaP	1440.0	-6.400000	2.486461e-05	-6.400600	-6.400000	-6.400000	-6.400000	-6.399800
P6	1440.0	27.999999	2.635231e-05	27.999000	28.000000	28.000000	28.000000	28.000000
Fair	1440.0	2.680753	4.127865e-03	2.622600	2.678700	2.680700	2.683000	2.699300
T3	1440.0	1564.149375	2.865350e+00	1555.800000	1562.200000	1564.100000	1566.000000	1573.500000
T2	1440.0	616.000063	7.019004e-03	615.980000	616.000000	616.000000	616.000000	616.020000
Tr	1440.0	969.001563	6.849410e-02	967.440000	968.980000	969.000000	969.020000	969.370000
Treg	1440.0	1249.965208	1.430992e-01	1248.800000	1249.900000	1250.000000	1250.000000	1250.100000
Lsp	1440.0	29.693318	1.550688e-01	27.519000	29.620000	29.698000	29.778000	30.236000
Tcyc	1440.0	1255.261111	1.539885e-01	1253.800000	1255.200000	1255.300000	1255.300000	1255.700000
Tcyc - Treg	1440.0	5.294570	8.754049e-02	4.489500	5.258475	5.286750	5.318325	5.881300
Cco,sg	1440.0	29875.119444	1.532124e+02	29012.000000	29838.000000	29875.000000	29916.000000	31371.000000
Co2,sg	1440.0	0.012540	3.653610e-04	0.009197	0.012382	0.012508	0.012643	0.015006
P5	1440.0	24.900000	6.752501e-13	24.900000	24.900000	24.900000	24.900000	24.900000
V4	1440.0	46.263708	3.639806e-01	45.379000	46.008750	46.373000	46.559000	46.840000
V6	1440.0	27.734301	1.285316e+00	24.580000	28.212000	28.342500	28.408000	28.813000
V7	1440.0	54.595515	8.454750e-02	53.401000	54.554000	54.595000	54.640250	54.975000
V3	1440.0	46.992527	1.936154e-02	46.924000	46.979000	46.991000	47.005000	47.078000
V1	1440.0	57.939251	2.065033e-01	57.343000	57.801000	57.934000	58.073000	58.619000
V2	1440.0	45.301624	8.714133e-02	45.013000	45.255000	45.295000	45.341000	46.261000
Frgc	1440.0	49594.152778	7.121163e+01	48812.000000	49549.750000	49592.000000	49637.000000	49971.000000
Fsc	1440.0	49593.238194	7.082435e+01	49072.000000	49549.000000	49590.000000	49636.250000	49970.000000
ACAB	1440.0	280.764625	1.370150e+00	275.270000	279.730000	281.165000	281.860000	282.770000
AWGC	1440.0	204.625465	2.503719e+00	198.600000	202.837500	205.385000	206.680000	208.490000
F5	1440.0	1990.668125	7.094397e+00	1970.200000	1985.900000	1990.500000	1995.300000	2014.000000
F7	1440.0	3737.294375	7.181960e+00	3636.300000	3733.900000	3737.200000	3740.900000	3769.700000
Fsg	1440.0	160.845076	2.493207e-01	157.320000	160.720000	160.840000	160.980000	161.960000
FV11	1440.0	28116.156250	2.742243e+02	27445.000000	27925.000000	28199.000000	28338.000000	28547.000000
P1	1440.0	14.637996	1.825900e-04	14.637000	14.638000	14.638000	14.638000	14.641000
P2	1440.0	35.041662	2.336692e-02	34.976000	35.027000	35.037000	35.055000	35.212000
FLPG	1440.0	3037.774583	4.419007e+01	2931.400000	3006.075000	3050.950000	3073.925000	3106.600000
FLN	1440.0	3917.950139	4.534886e+01	3853.200000	3878.675000	3904.650000	3953.225000	4029.600000
FHN	1440.0	708.818424	3.439050e+00	697.990000	706.720000	708.665000	710.982500	719.760000
FLCO	1440.0	1643.658125	3.754342e+00	1632.900000	1640.800000	1643.600000	1646.500000	1652.200000
FSlurry	1440.0	213.913611	3.699680e+00	205.930000	211.107500	214.075000	216.610000	224.070000
FReflux	1440.0	2984.433264	1.878808e+01	2938.600000	2972.300000	2987.000000	2996.500000	3029.200000
Tfra	1440.0	313.547090	1.204799e+00	310.710000	312.657500	313.895000	314.560000	315.330000
T10	1440.0	509.836076	1.319542e-01	509.500000	509.740000	509.840000	509.930000	510.150000
T20	1440.0	628.161611	1.590902e-01	627.840000	628.040000	628.140000	628.290000	628.470000
V9	1440.0	47.511276	1.506524e+00	45.383000	46.214000	47.051000	48.650250	51.247000
V8	1440.0	49.688417	1.914883e-01	49.287000	49.533750	49.700000	49.841000	50.131000
V10	1440.0	49.368097	4.820776e-01	47.879000	49.072000	49.343000	49.672250	50.901000
V11	1440.0	47.220298	2.061357e-01	46.630000	47.065750	47.218000	47.377000	47.686000

Leak in Main Air Blower to Regenerator Pipeline¶

Introduction¶

Data Preprocessing¶

Exploratory Data Analysis (EDA)¶

Anomaly Detection Techniques¶

Data Cleaning, Feature Engineering, Predictive Modeling¶

Testing the model on Varying feed flow rate.¶

Testing the model on abnormal dataset.¶

Visualization¶

Conclusion¶

Future Work¶

*Leak in Main Air Blower to Regenerator Pipeline*¶

Introduction¶

Data Preprocessing¶

Exploratory Data Analysis (EDA)¶

Anomaly Detection Techniques¶

Data Cleaning, Feature Engineering, Predictive Modeling¶

Testing the model on Varying feed flow rate.¶

Testing the model on abnormal dataset.¶

Visualization¶

Conclusion¶

Future Work¶

Leak in Main Air Blower to Regenerator Pipeline¶